Case Study: Reddit Social Network Analysis Against Influence Operation

Adel Abu Hashim - Oct 2020

Table of Contents

• Introduction
• Exploratory Data Analysis
• Conclusions

Introduction

This case study aims to help Amber Heard

By analyzing new accounts posting/ commenting against a victim of a Social Bot Disinformation/Influence Operation.

We have three main datasets:
(The datasets screaped from reddit).

• 1- A dataset with submissions & comments data (2018).
• 2- Users Data (from 2006 to 2018).
• 3- A merged dataset (submissions & comments data, users data).
• 4- Daily creation data (# of accounts created per day from 2006 to 2018)

#import dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import helpers
import matplotlib.dates as mdates
import plotly.express as px
import plotly.graph_objects as go
import re
import warnings
warnings.filterwarnings('ignore')
sb.set_style("darkgrid")
%matplotlib inline

# load data
df = pd.read_csv("cleaned_data/reddit_cleaned_2018.csv")
df_merged = pd.read_csv("cleaned_data/reddit_merged_2018.csv")

# convert to datetime
df.created_at = pd.to_datetime(df.created_at)
df_merged.created_at = pd.to_datetime(df_merged.created_at)
df_merged.user_created_at = pd.to_datetime(df_merged.user_created_at)

print(df.shape)
df.head(2);

(6993, 17)

print(df_merged.shape)
df_merged.head(2);

(6993, 24)

# Filter on banned accounts
df_banned = df_merged[df_merged['is_banned']]
print(df_banned.shape)
df_banned.head(2);

(586, 24)

len(df_banned.user_name.unique())

275

df_banned.submission_comment.value_counts()

submission    305
comment       281
Name: submission_comment, dtype: int64

Note: we only have user names for the banned accounts

# Filter on unverified accounts
df_unverified = df_merged[~df_merged['has_verified_email']]
print(df_unverified.shape)
df_unverified.head(2);

(789, 24)

len(df_unverified.user_name.unique())

495

# Filter on Accounts created in 2018
df_18 = df_merged[df_merged['user_created_at'].dt.year == 2018]
print(df_18.shape)
df_18.head(2);

(875, 24)

peak_day = '2018-07-03'

# Filter on Peak Day 
df_peak = df_merged[df_merged['created_at'].dt.date.astype('str') == peak_day]
df_peak_submissions = df_peak.query("submission_comment == 'submission'")

# Filter on Peak Day For Unverified accounts
df_unverified_peak = df_unverified[df_unverified['created_at'].dt.date.astype('str') == peak_day]
df_unverified_peak_submissions = df_unverified_peak.query("submission_comment == 'submission'")

# Filter on Submissions
df_submissions = df_merged.query("submission_comment == 'submission'")
df_unverified_submissions = df_unverified.query("submission_comment == 'submission'")

Exploratory Data Analysis

• Introduction
• Exploratory Data Analysis
• Conclusions

Banned / Unverified

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Contributions

• Contributions
• Peak Day Contributions
• Submissions
• Peak Day Submissions

colors = px.colors.qualitative.T10

fig = px.pie(df_merged.banned_unverified.value_counts().to_frame().reset_index(),
             values='banned_unverified', names='index', color_discrete_sequence = colors,
             title = 'Contributions of banned / unverified /others in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

df_merged.banned_unverified.value_counts().to_frame()

	banned_unverified
others	5618
unverified	789
banned	586

11.3+8.38

19.68

Peak Day Contributions

• Contributions
• Peak Day Contributions
• Submissions
• Peak Day Submissions

df_peak.banned_unverified.value_counts().to_frame()

	banned_unverified
others	136
banned	33
unverified	21

fig = px.pie(df_peak.banned_unverified.value_counts().to_frame().reset_index(),
             values='banned_unverified', names='index', color_discrete_sequence = colors,
             title = f'Contributions of banned / unverified /others on the peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

17.4+11.1

28.5

NOTE: 28.5% of peak day contributions were made by banned accounts and acoounts with unverified email address

Submissions

• Contributions
• Peak Day Contributions
• Submissions
• Peak Day Submissions

df_submissions.banned_unverified.value_counts().to_frame()

	banned_unverified
others	1572
banned	305
unverified	123

fig = px.pie(df_submissions.banned_unverified.value_counts().to_frame().reset_index(),
             values='banned_unverified', names='index', color_discrete_sequence = colors,
             title = 'Submissions of banned / unverified /others in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

round(15.3+6.15,2)

21.45

NOTE: About 21.45% of 2018 submissions were made by banned and unverified accounts.

Peak Day Submissions

• Contributions
• Peak Day Contributions
• Submissions
• Peak Day Submissions

df_peak_submissions.banned_unverified.value_counts().to_frame()

	banned_unverified
others	18
banned	10
unverified	2

fig = px.pie(df_peak_submissions.banned_unverified.value_counts().to_frame().reset_index(),
             values='banned_unverified', names='index', color_discrete_sequence = colors,
             title = f'Submissions of banned / unverified /others on the peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label')

fig.show()

total scores

df_merged.groupby('banned_unverified').sum('score')['score'].to_frame().reset_index()

	banned_unverified	score
0	banned	22477.0
1	others	217158.0
2	unverified	14718.0

fig = px.pie(df_merged.groupby('banned_unverified').sum('score')['score'].to_frame().reset_index(),
             values='score', names='banned_unverified', color_discrete_sequence = colors,
             title = 'Total Scores of banned/ unverified/ others in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Average Scores per contribution

df_merged.groupby('banned_unverified').mean('score')['score'].to_frame().reset_index()

	banned_unverified	score
0	banned	38.422222
1	others	38.688402
2	unverified	18.653992

fig = px.pie(df_merged.groupby('banned_unverified').mean('score')['score'].to_frame().reset_index(),
             values='score', names='banned_unverified', color_discrete_sequence = colors,
             title = 'Average Scores of banned/ unverified/ others in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Average Scores per submission

df_submissions.groupby('banned_unverified').mean('score')['score'].to_frame().reset_index()

	banned_unverified	score
0	banned	69.855263
1	others	118.459528
2	unverified	67.008130

fig = px.pie(df_submissions.groupby('banned_unverified').mean('score')['score'].to_frame().reset_index(),
             values='score', names='banned_unverified', color_discrete_sequence = colors,
             title = 'Average Scores submissions of banned/ unverified/ others in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Banned Accounts

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Banned-Accounts largest scores

• Banned-Accounts largest scores
• Banned-Accounts minimum scores


• Contributions of Banned accounts in 2020
• Banned Accounts Contributions Peaks

df_banned.score.describe()

count     585.000000
mean       38.422222
std       157.188792
min       -53.000000
25%         1.000000
50%         3.000000
75%        25.000000
max      2847.000000
Name: score, dtype: float64

# Filter on largest scores

df_scores_high = df_banned.sort_values('score', ascending=False).head(10)

fig = px.bar(df_scores_high,
             x='user_name', 
             y=df_scores_high.score, text = df_scores_high.score, title='Banned accounts highest contribution scores')

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_high.user_name,
    )
)

clrs = ['red' if (y > 2000) else '#5296dd' for y in df_scores_high.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')

# , marker_line_color='#5296dd'

fig.show()

df_scores_high.user_name

5748      fakeposter77
2589             CJ105
6629            88MPH1
4766     klaudiaschulz
1790    Tony_montana96
1793    Tony_montana96
2013         Series000
4322        vonmark955
2244     horny_fuckers
4341        GWhisperer
Name: user_name, dtype: object

NOTE: "fakeposter77": this user contributions got the hieghest scores.
(Since this user is banned, we have no user information but, we can further investigate his contributions)

df_crack = df_banned[df_banned.user_name == 'fakeposter77'].sort_values('created_at')
df_crack.head()

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
5748	t3_9w7ir0	/r/celebnsfw/comments/9w7ir0/amber_heard_in_th...	Amber Heard in The Informers	NaN	r/celebnsfw	2018-11-11 21:11:41	Neutral	Neutral	2847.0	NaN	...	True	True	True	NaN	NaN	NaT	banned	banned	NaN	NaN

1 rows × 24 columns

df_crack.submission_comment.value_counts()

submission    1
Name: submission_comment, dtype: int64

NOTE: "fakeposter77": only one submission in 11-11-2018.

df_crack.text.value_counts()

Amber Heard in The Informers    1
Name: text, dtype: int64

Banned-Accounts minimum scores

• Banned-Accounts largest scores
• Banned-Accounts minimum scores


• Contributions of Banned accounts in 2018
• Banned Accounts Contributions Peaks

https://stackoverflow.com/questions/67590028/plotly-how-to-move-x-axis-to-top-and-y-axis-to-the-right

# Filter on minimum scores

df_scores_low = df_banned.sort_values('score').head(20)

fig = px.bar(df_scores_low,
             x='user_name', 
             y=df_scores_low.score, 
             text = df_scores_low.score)

fig.update_layout(title_text='Banned accounts minimum contribution scores', title_x=0.5, title_y=0.2)

fig.update_layout(
    xaxis = dict(
        side='top',
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_low.user_name,
    )
)

clrs = ['red' if (y < -50) else '#5296dd' for y in df_scores_low.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_scores_low.user_name

6384          DrinkingKratom
6948      thebloodyaugustABC
5671    ThisWasPrettyAnoying
6661                bigbrycm
5672    ThisWasPrettyAnoying
4467     ___0047532899532___
6886          ElegantCyclist
3613           Ride0rDie2020
5898           KOOL69THUGBOI
3615           Ride0rDie2020
3614           Ride0rDie2020
5372         FuckingObsessed
3620           Ride0rDie2020
2173              Gaultier55
5562              GoldPisseR
4701        JesusSkywalkered
5744                Maudraum
3621           Ride0rDie2020
6480               NecroHexr
6660              boobsRlyfe
Name: user_name, dtype: object

df_pp = df_banned[df_banned.user_name == 'DrinkingKratom']
df_pp.sort_values('created_at', inplace=True);

df_pp

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
6384	t1_ebzvfsh	/r/crappycontouring/comments/a72jn4/personally...	or she could not wear makeup	t1_ebzssvn	r/crappycontouring	2018-12-17 19:31:53	Neutral	Neutral	-53.0	comment	...	True	True	True	NaN	NaN	NaT	banned	banned	NaN	NaN

1 rows × 24 columns

df_pp.submission_comment.value_counts()

comment    1
Name: submission_comment, dtype: int64

# df_amazing.permalink[15599]

Contributions of Banned accounts in 2018

• Explore the largest scores
• Explore the minimum scores


• Contributions of Banned accounts in 2018
• Banned Accounts Contributions Peaks

Contributions

fig = px.pie(df_merged.is_banned.value_counts(),
             values='is_banned', names=['others', 'banned'], color_discrete_sequence = colors,
             title = 'Contributions of Banned accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

banned_contr_prop = df_banned.shape[0] * 100 /df_merged.shape[0]
banned_contr_prop
print('The percentage % of 2018 contributions made by Banned accounts: ', round(banned_contr_prop, 2))

The percentage % of 2018 contributions made by Banned accounts:  8.38

print('Total banned accounts contributions in 2018: ', df_banned.shape[0])

Total banned accounts contributions in 2018:  586

Comments

print('Total banned accounts comments in 2018: ',df_banned.query(" submission_comment == 'comment' ").shape[0])

Total banned accounts comments in 2018:  281

df_banned.query(" submission_comment == 'comment' ").text.value_counts().head(5)

[GIFV link](https://i.imgur.com/LRhwNvS.gifv)\r\n\r\n---\r\n\r\n_^I ^am ^a ^bot. ^[FAQ](https://www.reddit.com/r/livven/wiki/gifv-bot) ^// ^[code](https://github.com/Livven/GifvBot)_    3
Unzip her and have her bend over slightly, bracing against the window.  Put on a show for everyone going by...                                                                            2
r/celebrityasses                                                                                                                                                                          2
/r/Amber_Heard.                                                                                                                                                                           2
I guess. But I still don't like the idea of being the enemy to stop the enemy,\n\nBut it is very true that being a good boy doesn't grt you cookies.                                      1
Name: text, dtype: int64

Submissions

fig = px.pie(df_submissions.is_banned.value_counts(),
             values='is_banned', names=['others', 'banned'], color_discrete_sequence = colors,
             title = 'Submissions of Banned accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

banned_sub_prop = df_banned.query(" submission_comment == 'submission' ").shape[0] * 100\
/df_merged.query(" submission_comment == 'submission' ").shape[0]
print('The percentage % of 2018 submissions made by Banned accounts: ', banned_sub_prop)

The percentage % of 2018 submissions made by Banned accounts:  15.25

print('Total banned accounts submissions in 2018: ', df_banned.query(" submission_comment == 'submission' ").shape[0])

Total banned accounts submissions in 2018:  305

df_banned.query(" submission_comment == 'submission' ").text.value_counts().head(5)

Amber Heard                           142
Pinned to Amber Heard on Pinterest      8
Amber Heard - The Informers             8
Amber Heard - London Fields             5
Amber Heard & Amanda Seyfried           3
Name: text, dtype: int64

txt = 'Amber Heard'
df_banned[df_banned['text'] == txt][['user_name', 'subreddit', 'created_at']]

# Nothe that we don't have data on banned users except for their names

	user_name	subreddit	created_at
1790	Tony_montana96	r/Celebs	2018-01-12 19:01:12
1791	Tony_montana96	r/celebritylegs	2018-06-15 09:27:36
1792	Tony_montana96	r/CelebrityArmpits	2018-07-13 20:17:59
1793	Tony_montana96	r/Celebs	2018-07-30 20:22:36
2013	Series000	r/goddesses	2018-01-23 18:41:17
...	...	...	...
6840	tinjaw314w	r/Celebs	2018-12-26 05:30:05
6869	MobileDon	r/Celebs	2018-12-27 01:35:04
6870	ChemicalDuty	r/BeautifulFemales	2018-12-27 01:35:31
6915	Celebrity-Pix	r/celebrities	2018-12-29 01:25:59
6976	BlondeLegion	r/AltBlonde	2018-12-31 15:40:50

142 rows × 3 columns

df_banned[df_banned['text'] == txt][['user_name', 'subreddit', 'created_at']].groupby('user_name').count().sort_values('subreddit', ascending=False).head(11)

	subreddit	created_at
user_name
vonmark955	13	13
naughtytwd	11	11
horny_fuckers	10	10
demonicmax56	9	9
vonjobi951	9	9
Pm-me-your-ass-photo	7	7
OrganicSatisfaction	5	5
CJ105	4	4
chrisdeli	4	4
Tony_montana96	4	4
PussySlayingVirgin	4	4

df_banned[df_banned['user_name'] == 'vonmark955'].subreddit.value_counts()

r/Celebsnudess        10
r/Amber_Heard          2
r/CelebSexScenes       2
r/CelebrityNipples     2
r/Celebhub             1
r/celebnsfw            1
r/Celebs               1
r/CelebrityButts       1
Name: subreddit, dtype: int64

df_banned[df_banned['user_name'] == 'vonmark955'].created_at.dt.date.value_counts()

2018-08-27    6
2018-08-15    3
2018-07-24    2
2018-10-05    2
2018-07-31    1
2018-08-28    1
2018-09-29    1
2018-07-22    1
2018-09-28    1
2018-09-25    1
2018-07-25    1
Name: created_at, dtype: int64

df_banned[df_banned['user_name'] == 'naughtytwd'].subreddit.value_counts()

r/Celebs        11
r/geekboners     1
Name: subreddit, dtype: int64

df_banned[df_banned['user_name'] == 'naughtytwd'].created_at.dt.date.value_counts()

2018-06-14    4
2018-06-03    3
2018-07-23    1
2018-06-05    1
2018-07-21    1
2018-07-24    1
2018-06-16    1
Name: created_at, dtype: int64

df_banned[df_banned['user_name'] == 'horny_fuckers'].subreddit.value_counts()

r/celebritylegs      9
r/Celebs             1
r/gentlemanboners    1
Name: subreddit, dtype: int64

df_banned[df_banned['user_name'] == 'horny_fuckers'].created_at.dt.date.value_counts()

2018-05-11    2
2018-04-08    1
2018-02-13    1
2018-05-23    1
2018-05-02    1
2018-05-05    1
2018-03-21    1
2018-04-16    1
2018-03-26    1
2018-03-11    1
Name: created_at, dtype: int64

txt = """Amber Heard Flashing Boobs While Cleaning Up In Her Garage"""
rod = df_banned[df_banned['text'] == txt][['user_name', 'subreddit', 'created_at']]
rod
# Nothe that we don't have data on banned users except for their names

	user_name	subreddit	created_at
4735	michikoperdue	r/CelebNudes	2018-08-01 19:14:53
4736	michikoperdue	r/Celebsnudess	2018-08-01 19:17:19
4737	michikoperdue	r/celebJObuds	2018-08-01 19:18:18

rod.user_name.value_counts()

michikoperdue    3
Name: user_name, dtype: int64

rod.subreddit.value_counts()

r/CelebNudes      1
r/Celebsnudess    1
r/celebJObuds     1
Name: subreddit, dtype: int64

rod.created_at.dt.date.value_counts()

2018-08-01    3
Name: created_at, dtype: int64

NOTE: "michikoperdue": this user made 3 contributions with the same text in the same time in 3 different subreddits

• r/Celebsnudess 1
• r/CelebNudes 1
• r/celebJObuds 1

Banned Accounts Contributions Peaks

• Explore the largest scores
• Explore the minimum scores


• Contributions of Banned accounts in 2020
• Banned Accounts Contributions Peaks

Which dates had the highest contrbitions of banned users?

# group by date an count
banned_contributions = df_banned.groupby(df_banned.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(banned_contributions,
             x='created_at', y='n_contributions')

fig.update_layout(
            title={
        'text': "The number of banned users contributions created on each date",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=.5, opacity=1, textposition='auto').update_layout()
fig.show()

banned_contributions.sort_values('n_contributions', ascending=False).head(10)

	created_at	n_contributions
84	2018-07-03	33
95	2018-07-24	19
191	2018-12-20	18
120	2018-08-27	18
93	2018-07-22	17
190	2018-12-19	12
157	2018-11-08	11
136	2018-09-29	11
113	2018-08-16	9
85	2018-07-04	9

# sort by n_contributions, take the top 3, then sort them by date 
banned_contributions.sort_values('n_contributions', ascending=False, inplace=True)
banned_trendy = banned_contributions.head(5)
banned_trendy.sort_values('created_at', inplace=True)

fig = px.bar(banned_trendy,
             x='created_at', y='n_contributions')

fig.update_layout(
            title={
        'text': "The number of banned users contributions on peak dates",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = banned_trendy.created_at,
    )
)

clrs = ['red' if (y > 60) else '#5296dd' for y in banned_trendy['n_contributions']] 

fig.update_traces(marker_color=clrs,
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()

banned_trendy

	created_at	n_contributions
84	2018-07-03	33
93	2018-07-22	17
95	2018-07-24	19
120	2018-08-27	18
191	2018-12-20	18

Unverified Accounts

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Explore unverified accounts with the largest link karma

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

link_high_users = df_unverified.sort_values('link_karma', ascending=False).user_name.unique()[:10]

df_link_high = df_unverified.sort_values('link_karma', ascending=False)\
[df_unverified.sort_values('link_karma', ascending=False)\
 .user_name.isin(link_high_users)]

# Filter on largest link karma

#df_link_high = df_unverified.sort_values('link_karma', ascending=False).head(10)

fig = px.bar(df_link_high,
             x='user_name', 
             y=df_link_high.link_karma, text = df_link_high.link_karma, title='Unverified accounts with highest link karma')

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_link_high.user_name,
    )
)

clrs = ['red' if (y > 1000000) else '#5296dd' for y in df_link_high.link_karma]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')

# , marker_line_color='#5296dd'

fig.show()

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_unverified[df_unverified.user_name == 'Shutupvoices'].head(1))

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2665	t3_84v4ag	/r/gentlemanboners/comments/84v4ag/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-03-16 12:41:49	Neutral	Neutral	166.0	NaN	submission	amber_heard	Shutupvoices	False	False	False	False	20758.0	1008564.0	2017-07-02 02:35:59	unverified	others	257 days 10:05:50	257.0

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_unverified[df_unverified.user_name == 'Nergaal'].head(1))

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
5093	t3_984rpq	/r/nottheonion/comments/984rpq/johnny_depp_accuses_ex_wife_amber_heard_of_pooing/	Johnny Depp accuses ex wife Amber Heard of pooing in their bed	NaN	r/nottheonion	2018-08-17 18:34:03	Neutral	Negative	11.0	NaN	submission	johnny_depp_accuses_ex_wife_amber_heard_of_pooing	Nergaal	False	True	False	False	119065.0	920951.0	2015-04-10 14:36:16	unverified	others	1225 days 03:57:47	1225.0

Explore unverified accounts with the largest comment karma

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

link_high_users = df_unverified.sort_values('comment_karma', ascending=False).user_name.unique()[:10]

df_comment_high = df_unverified.sort_values('comment_karma', ascending=False)\
[df_unverified.sort_values('link_karma', ascending=False)\
 .user_name.isin(link_high_users)]

# Filter on largest comment karma

fig = px.bar(df_comment_high,
             x='user_name', 
             y=df_comment_high.comment_karma, text = df_comment_high.comment_karma, title='Unverified accounts with highest comment karma')

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_comment_high.user_name,
    )
)

clrs = ['red' if (y > 900000) else '#5296dd' for y in df_comment_high.comment_karma]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')

# , marker_line_color='#5296dd'

fig.show()

df_unverified[df_unverified.user_name == 'hnglmkrnglbrry'].user_created_at

6892   2017-01-30 18:40:33
Name: user_created_at, dtype: datetime64[ns]

df_unverified[df_unverified.user_name == 'hnglmkrnglbrry']

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
6892	t1_ecoi1it	/r/politics/comments/aa2auy/amber_heard_i_spok...	Did you just r/gatekeeping domestic violence?	t1_ecoff25	r/politics	2018-12-27 20:25:46	Neutral	Negative	16.0	comment	...	False	True	False	995853.0	721.0	2017-01-30 18:40:33	unverified	others	696 days 01:45:13	696.0

1 rows × 24 columns

Account Created at: 2017-01-30

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_unverified[df_unverified.user_name == 'sysadminbj'].head(1))

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
5517	t1_e8pvhc1	/r/movies/comments/9smlbt/amber_heard_film_london_fields_suffers_one_of_the/e8pvhc1/	I thought that sounded familiar.	t1_e8pv4sn	r/movies	2018-10-30 11:09:12	Positive	Neutral	2.0	comment	comment	amber_heard_film_london_fields_suffers_one_of_the	sysadminbj	False	False	False	False	632535.0	1073.0	2010-10-26 14:20:57	unverified	others	2925 days 20:48:15	2925.0

hnglmkrnglbrry unverified account with the highest comment karma.

• Account Created at: 2017-01-30

Explore unverified accounts with the minimum link karma

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

# Filter on minimum link karma

df_link_low = df_unverified.sort_values('link_karma').head(20)

fig = px.bar(df_link_low,
             x='user_name', 
             y=df_link_low.link_karma, 
             text = df_link_low.link_karma)

fig.update_layout(title_text='Unverified accounts with minimum link karma', title_x=0.5)

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_link_low.user_name,
    )
)

clrs = ['red' if (y < -60) else '#5296dd' for y in df_link_low.link_karma]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_link_low.user_name.unique()

array(['nsfwalt_100', 'EverGenius', 'windinmynuts',
       'giantfatdelicousbird', 'epicimagebot', 'IWannaBangBiel',
       'IamsWhoIams', 'Tedmonds30', 'juggalobobby1', 'Sonmeisterbank',
       'MrGreen098', 'Ragobie0404'], dtype=object)

A lot of unverified users has a link karma of 1

• 'nsfwalt_100', 'EverGenius', 'windinmynuts',

   'giantfatdelicousbird', 'giantfatdelicousbird',
   'giantfatdelicousbird', 'epicimagebot', 'giantfatdelicousbird',
   'giantfatdelicousbird', 'IWannaBangBiel', 'IWannaBangBiel',
   'IWannaBangBiel', 'IWannaBangBiel', 'IWannaBangBiel',
   'IamsWhoIams', 'Tedmonds30', 'juggalobobby1', 'Sonmeisterbank',
   'MrGreen098', 'Ragobie0404'

Explore unverified accounts with the minimum comment karma

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

# Filter on minimum comment karma

df_comment_low = df_unverified.sort_values('comment_karma').head(20)

fig = px.bar(df_comment_low,
             x='user_name', 
             y=df_comment_low.comment_karma, 
             text = df_comment_low.comment_karma)

fig.update_layout(title_text='Unverified accounts with minimum comment karma', title_x=0.5, title_y=0.1)

fig.update_layout(
    xaxis = dict(
        side='top',
        title='user name',
        tickmode = 'array',
        tickvals = df_comment_low.user_name,
    )
)

clrs = ['red' if (y < -30) else '#5296dd' for y in df_comment_low.comment_karma]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show() 

df_unverified[df_unverified.user_name == 'murph420']

# Defending AH

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2920	t1_dx71umk	/r/CelebrityFeet/comments/8bg153/amber_heard/d...	What did she hear?	t3_8bg153	r/CelebrityFeet	2018-04-11 17:43:44	Neutral	Neutral	2.0	submission	...	False	False	False	-3.0	9.0	2016-08-27 10:11:29	unverified	others	592 days 07:32:15	592.0

1 rows × 24 columns

df_unverified[df_unverified.user_name == 'decreased_beast']

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
4519	t1_e2ykiew	/r/celebnsfw/comments/91h1om/amber_heard/e2ykiew/	I bet u like armpits .. i would love to lick hers	t1_e2ycjwe	r/celebnsfw	2018-07-24 18:37:34	Positive	Positive	-5.0	comment	...	False	False	False	-3.0	1.0	2017-06-17 20:51:16	unverified	others	401 days 21:46:18	401.0

1 rows × 24 columns

df_unverified[df_unverified.user_name == 'cozixemera']

# Defending AH

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
1931	t3_7q5eif	/r/SubredditDrama/comments/7q5eif/rharrypotter...	r/harrypotter Duel on the topic of 'domestic a...	NaN	r/SubredditDrama	2018-01-13 16:30:05	Neutral	Negative	1.0	NaN	...	False	False	False	-2.0	1.0	2018-01-13 16:26:14	unverified	2018	0 days 00:03:51	0.0

1 rows × 24 columns

murph420 has the minimum comment karma

Explore the largest scores

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

df_unverified.score.describe()

count    789.000000
mean      18.653992
std       55.205046
min      -64.000000
25%        1.000000
50%        3.000000
75%       11.000000
max      578.000000
Name: score, dtype: float64

# Filter on largest scores

df_scores_high = df_unverified.sort_values('score', ascending=False).head(10)

fig = px.bar(df_scores_high,
             x='user_name', 
             y=df_scores_high.score, text = df_scores_high.score, title='Unverified accounts with highest contribution scores in 2018')

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_high.user_name,
    )
)

clrs = ['red' if (y > 500) else '#5296dd' for y in df_scores_high.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_scores_high.user_name.head(2)

2927    ibracadabra101
5850        skwolvie41
Name: user_name, dtype: object

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_unverified[df_unverified.user_name == 'ibracadabra101'].head(1))

# Negative Submission

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2927	t3_8c1p7w	/r/Celebs/comments/8c1p7w/amber_heard/	Amber Heard	NaN	r/Celebs	2018-04-13 19:03:16	Neutral	Neutral	578.0	NaN	submission	amber_heard	ibracadabra101	False	False	False	False	47.0	1914.0	2017-03-16 17:16:16	unverified	others	393 days 01:47:00	393.0

with pd.option_context('display.max_colwidth', None):
  display(df_unverified[df_unverified.user_name == 'skwolvie41'].head(1))

# Negative Submissions and Comments

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
5849	t3_9xspao	/r/Celebs/comments/9xspao/amber_heard/	Amber Heard	NaN	r/Celebs	2018-11-17 02:27:32	Neutral	Neutral	101.0	NaN	...	False	False	False	25360.0	24147.0	2016-07-26 09:07:11	unverified	others	843 days 17:20:21	843.0

1 rows × 24 columns

ibracadabra101 this user has the highest score among the unverified users

Explore the minimum scores

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

# Filter on minimum scores

df_scores_low = df_unverified.sort_values('score').head(20)

fig = px.bar(df_scores_low,
             x='user_name', 
             y=df_scores_low.score, 
             text = df_scores_low.score)

fig.update_layout(title_text='Unverified accounts with minimum contribution scores in 2018', title_x=0.5, title_y=0.2)

fig.update_layout(
    xaxis = dict(
        side='top',
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_low.user_name,
    )
)

clrs = ['red' if (y < -45) else '#5296dd' for y in df_scores_low.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_scores_low.user_name.head(3)

3856     HardSellDude
4672       gixxerface
2481    moc_moc_a_moc
Name: user_name, dtype: object

df_unverified[df_unverified.user_name == 'HardSellDude']

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
3856	t1_e1g3qfy	/r/gentlemanboners/comments/8uiuca/amber_heard...	I prefer brunettes with small tits	t3_8uiuca	r/gentlemanboners	2018-06-28 16:34:37	Negative	Negative	-64.0	submission	...	True	False	False	16666.0	4836.0	2017-09-13 20:43:54	unverified	others	287 days 19:50:43	287.0

1 rows × 24 columns

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_unverified[df_unverified.user_name == 'gixxerface'].head(1))

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
4672	t1_e3duttt	/r/entertainment/comments/93iyoe/johnny_depp_claims_ex_amber_heard_punched_him/e3duttt/	I would let her punch me in the face as many times she wants. As long as make up sex follows	t3_93iyoe	r/entertainment	2018-08-01 00:36:29	Positive	Neutral	-54.0	submission	comment	johnny_depp_claims_ex_amber_heard_punched_him	gixxerface	False	False	False	False	225.0	33.0	2017-07-04 00:21:41	unverified	others	393 days 00:14:48	393.0

with pd.option_context('display.max_colwidth', None):
  display(df_unverified[df_unverified.user_name == 'moc_moc_a_moc'].head(2))

# Defending AH

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2480	t1_dvdldd9	/r/gentlemanboners/comments/82wa5c/amber_heard/dvdldd9/	She's donating her entire divorce settlement from Depp to charity. If she's supposed to be a gold digger she's not a very good one.	t1_dvddsca	r/gentlemanboners	2018-03-08 14:58:53	Negative	Neutral	161.0	comment	...	False	False	False	20029.0	172.0	2016-04-19 18:49:26	unverified	others	687 days 20:09:27	687.0
2481	t1_dzdr3xz	/r/gentlemanboners/comments/8l6x7k/amber_heard/dzdr3xz/	Yawn, somebody always brings this bullshit up. What kind of gold digger donates their entire divorce settlement to charity?\n\nFOH with the dumb misogynist insults.	t1_dzdke3y	r/gentlemanboners	2018-05-22 11:52:16	Positive	Neutral	-50.0	comment	...	False	False	False	20029.0	172.0	2016-04-19 18:49:26	unverified	others	762 days 17:02:50	762.0

2 rows × 24 columns

HardSellDude this user has the minimum score among the unverified users

Contributions of Unverified accounts in 2018

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

Unverified accounts contributions in 2018

Contributions

fig = px.pie(df_merged.has_verified_email.value_counts(),
             values='has_verified_email', names=['verified', 'unverified'], color_discrete_sequence = colors,
             title = 'Contributions of Unverified accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

unverified_contr_prop = df_unverified.shape[0] * 100 /df_merged.shape[0]

print('The percentage % of 2018 contributions made by Unverified accounts: ', round(unverified_contr_prop,2))

The percentage % of 2018 contributions made by Unverified accounts:  11.28

print('Total unverified accounts contributions in 2018: ', df_unverified.shape[0])

Total unverified accounts contributions in 2018:  789

peak_day

'2018-07-03'

Peak day contributions 2018-07-03

fig = px.pie(df_peak.has_verified_email.value_counts(),
             values='has_verified_email', names=['verified', 'unverified'], color_discrete_sequence = colors,
             title = f'Contributions of Unverified accounts on peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Comments

print('Total banned accounts comments in 2018: ', df_unverified.query(" submission_comment == 'comment' ").shape[0])

Total banned accounts comments in 2018:  666

df_unverified.query(" submission_comment == 'comment' ").text.value_counts().head(5)

What did she hear?    3
MFK                   2
What is this from?    2
r/Lordosis            2
Mfk                   2
Name: text, dtype: int64

Submissions

fig = px.pie(df_submissions.has_verified_email.value_counts(),
             values='has_verified_email', names=['verified', 'unverified'], color_discrete_sequence = colors,
             title = 'Submissions of Unverified accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

unverified_sub_prop = df_unverified.query(" submission_comment == 'submission' ").shape[0] * 100\
/df_merged.query(" submission_comment == 'submission' ").shape[0]

print('The percentage % of 2018 submissions made by Unverified accounts: ', unverified_sub_prop)

The percentage % of 2018 submissions made by Unverified accounts:  6.15

print('Total unverified accounts submissions in 2018: ', df_unverified.query(" submission_comment == 'submission' ").shape[0])

Total unverified accounts submissions in 2018:  123

df_unverified.query(" submission_comment == 'submission' ").text.value_counts().head(5)

Amber Heard                                                                      49
LONDON FIELDS Official Trailer (2018) Amber Heard, Johnny Depp Movie HD           5
Amber Heard: I Spoke Up Against Sexual Violence and Faced Our Culture's Wrath     2
amber heard lifestyle                                                             2
Edited: Amber Heard, Gal Gadot and Margot Robbie                                  2
Name: text, dtype: int64

txt = 'Amber Heard'
df_unverified[df_unverified['text'] == txt][['user_name', 'user_created_at', 'submission_comment', 'created_at', 'subreddit']]

# Note that we don't have data on banned users except for their names.

	user_name	user_created_at	submission_comment	created_at	subreddit
2665	Shutupvoices	2017-07-02 02:35:59	submission	2018-03-16 12:41:49	r/gentlemanboners
2770	ClementePark	2015-08-27 01:44:37	submission	2018-03-26 18:43:27	r/Celebs
2805	SilviaJu	2018-02-14 03:47:09	submission	2018-03-28 21:12:21	r/Celebs
2825	ilikeskirt	2016-02-11 22:04:59	submission	2018-03-29 13:23:28	r/SlitDresses
2927	ibracadabra101	2017-03-16 17:16:16	submission	2018-04-13 19:03:16	r/Celebs
3006	arcane84	2016-01-08 05:51:38	submission	2018-12-09 07:39:38	r/gentlemanboners
3007	arcane84	2016-01-08 05:51:38	submission	2018-12-09 10:25:11	r/Celebs
3626	ScaredBite5	2018-06-07 21:14:20	submission	2018-06-07 21:25:47	r/CelebsInTights
4218	Divafap1	2016-07-05 08:09:07	submission	2018-07-12 04:35:42	r/Celebs
4219	Divafap1	2016-07-05 08:09:07	submission	2018-07-25 22:31:58	r/Celebs
4223	Divafap1	2016-07-05 08:09:07	submission	2018-08-25 05:31:20	r/Celebs
4307	Nbaslamindub	2018-07-21 21:21:27	submission	2018-07-21 21:22:03	r/Celebs
5299	LOVESPANTYHOSEDWOMEN	2017-01-05 19:10:04	submission	2018-09-18 11:38:39	r/CelebsInTights
5369	Win168k	2018-08-03 17:35:12	submission	2018-10-04 18:19:49	u/Win168k
5370	Win168k	2018-08-03 17:35:12	submission	2018-10-26 18:14:26	u/Win168k
5460	Jtondi07	2016-09-04 16:25:26	comment	2018-10-23 15:19:55	r/pickoneceleb
5463	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:27:40	r/gentlemanboners
5464	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:27:40	r/Celebs
5465	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:29:03	r/sexycelebs
5466	DowntownHand	2018-09-28 20:18:22	submission	2018-10-23 19:27:45	r/Celebhub
5467	DowntownHand	2018-09-28 20:18:22	submission	2018-10-23 19:27:51	r/goddesses
5496	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 15:30:08	r/sexycelebs
5497	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 16:13:01	r/goddesses
5498	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-15 10:53:06	r/sexycelebs
5499	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-22 10:53:19	r/sexycelebs
5500	CelebsSpot	2018-10-26 06:12:58	submission	2018-12-15 18:26:03	r/sexycelebs
5591	prettycoolu	2018-09-12 10:31:32	submission	2018-11-02 11:03:10	r/PortraitsPorn
5716	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-10 09:16:46	r/Celebhub
5717	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-23 10:41:32	r/Celebhub
5718	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 13:46:12	r/Celebhub
5719	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:05	r/Celebhub
5720	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:22	r/Celebhub
5721	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:11:35	r/Celebhub
5722	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:27:53	r/Celebhub
5723	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 15:05:37	r/Celebhub
5794	unimasop	2018-09-20 20:55:11	submission	2018-11-13 02:32:12	r/Celebs
5795	unimasop	2018-09-20 20:55:11	submission	2018-11-13 02:32:16	r/Celebhub
5833	jackslater55	2018-09-15 09:10:33	submission	2018-11-15 17:55:33	r/Celebs
5834	jackslater55	2018-09-15 09:10:33	submission	2018-11-27 09:42:24	r/Celebs
5844	newwavedude	2015-01-12 08:48:57	submission	2018-11-16 15:33:20	r/Celebs
5849	skwolvie41	2016-07-26 09:07:11	submission	2018-11-17 02:27:32	r/Celebs
5850	skwolvie41	2016-07-26 09:07:11	submission	2018-12-27 11:02:09	r/Celebs
6229	mostepsic	2018-09-20 02:02:22	submission	2018-12-12 23:45:46	r/Celebs
6364	BekerBroun	2013-10-19 05:00:15	submission	2018-12-16 12:50:16	r/gentlemanboners
6681	girlnamedmartin	2018-02-27 02:50:42	submission	2018-12-23 04:04:13	r/PrettyGirls
6735	pietten	2016-12-27 02:15:46	submission	2018-12-21 07:05:47	r/gentlemanboners
6736	pietten	2016-12-27 02:15:46	submission	2018-12-21 07:07:06	r/Celebhub
6908	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-28 19:33:55	r/gentlemanboners
6909	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-28 19:34:47	r/Celebs
6910	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-30 10:30:49	r/Celebs

temp = df_unverified[df_unverified['text'] == txt][['user_name', 'user_created_at', 'submission_comment', 'created_at', 'subreddit']]

temp[temp.user_name.isin(temp.user_name.value_counts().head(6).index.values)]

	user_name	user_created_at	submission_comment	created_at	subreddit
4218	Divafap1	2016-07-05 08:09:07	submission	2018-07-12 04:35:42	r/Celebs
4219	Divafap1	2016-07-05 08:09:07	submission	2018-07-25 22:31:58	r/Celebs
4223	Divafap1	2016-07-05 08:09:07	submission	2018-08-25 05:31:20	r/Celebs
5463	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:27:40	r/gentlemanboners
5464	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:27:40	r/Celebs
5465	elect99	2017-04-30 09:32:16	submission	2018-10-23 19:29:03	r/sexycelebs
5496	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 15:30:08	r/sexycelebs
5497	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 16:13:01	r/goddesses
5498	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-15 10:53:06	r/sexycelebs
5499	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-22 10:53:19	r/sexycelebs
5500	CelebsSpot	2018-10-26 06:12:58	submission	2018-12-15 18:26:03	r/sexycelebs
5716	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-10 09:16:46	r/Celebhub
5717	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-23 10:41:32	r/Celebhub
5718	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 13:46:12	r/Celebhub
5719	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:05	r/Celebhub
5720	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:22	r/Celebhub
5721	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:11:35	r/Celebhub
5722	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:27:53	r/Celebhub
5723	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 15:05:37	r/Celebhub
6735	pietten	2016-12-27 02:15:46	submission	2018-12-21 07:05:47	r/gentlemanboners
6736	pietten	2016-12-27 02:15:46	submission	2018-12-21 07:07:06	r/Celebhub
6908	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-28 19:33:55	r/gentlemanboners
6909	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-28 19:34:47	r/Celebs
6910	corporate_monster777	2018-11-14 17:24:55	submission	2018-12-30 10:30:49	r/Celebs

temp[temp.user_name.isin(temp.user_name.value_counts().head(6).index.values)][temp.user_name == 'Doc-Sidious']

	user_name	user_created_at	submission_comment	created_at	subreddit
5716	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-10 09:16:46	r/Celebhub
5717	Doc-Sidious	2018-11-05 14:28:02	submission	2018-11-23 10:41:32	r/Celebhub
5718	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 13:46:12	r/Celebhub
5719	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:05	r/Celebhub
5720	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:09:22	r/Celebhub
5721	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:11:35	r/Celebhub
5722	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 14:27:53	r/Celebhub
5723	Doc-Sidious	2018-11-05 14:28:02	submission	2018-12-02 15:05:37	r/Celebhub

temp[temp.user_name.isin(temp.user_name.value_counts().head(6).index.values)][temp.user_name == 'Doc-Sidious'].created_at.dt.date.value_counts()

2018-12-02    6
2018-11-10    1
2018-11-23    1
Name: created_at, dtype: int64

user Doc-Sidious made 8 submissions in the same subreddit r/Celebhub ,6 of them were made in 2018-12-02

• 2018-12-02 6
• 2018-11-23 1
• 2018-11-10 1

df_unverified[df_unverified['user_name'] == 'Doc-Sidious'].head(1)[[ 'user_name',
       'has_verified_email', 'is_mod', 'is_gold', 'is_banned', 'comment_karma',
       'link_karma', 'user_created_at', 'banned_unverified', 'creation_year',
        'days_after_creation']]

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	days_after_creation
5716	Doc-Sidious	False	False	False	False	99.0	44851.0	2018-11-05 14:28:02	unverified	2018	4.0

temp[temp.user_name.isin(temp.user_name.value_counts().head(6).index.values)][temp.user_name == 'CelebsSpot']

	user_name	user_created_at	submission_comment	created_at	subreddit
5496	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 15:30:08	r/sexycelebs
5497	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-12 16:13:01	r/goddesses
5498	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-15 10:53:06	r/sexycelebs
5499	CelebsSpot	2018-10-26 06:12:58	submission	2018-11-22 10:53:19	r/sexycelebs
5500	CelebsSpot	2018-10-26 06:12:58	submission	2018-12-15 18:26:03	r/sexycelebs

temp[temp.user_name.isin(temp.user_name.value_counts().head(6).index.values)][temp.user_name == 'CelebsSpot'].created_at.dt.date.value_counts()

2018-11-12    2
2018-11-22    1
2018-11-15    1
2018-12-15    1
Name: created_at, dtype: int64

df_unverified[df_unverified['user_name'] == 'CelebsSpot'].head(1)[[ 'user_name',
       'has_verified_email', 'is_mod', 'is_gold', 'is_banned', 'comment_karma',
       'link_karma', 'user_created_at', 'banned_unverified', 'creation_year',
        'days_after_creation']]

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	days_after_creation
5495	CelebsSpot	False	True	False	False	250.0	42250.0	2018-10-26 06:12:58	unverified	2018	3.0

user CelebsSpot made 5 submissions, 4 of them in one subreddit r/sexycelebs

• 2018-11-12 2
• 2018-11-15 1
• 2018-11-22 1
• 2018-12-15 1

The account creation year of the unverified accounts contributed in 2018

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

Contributions

fig = px.pie(df_unverified.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = 'Contributions of Unverified accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

fig = px.pie(df_unverified_peak.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = f'Contributions of unverified accounts on the peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Submissions

fig = px.pie(df_unverified_submissions.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = 'Submissions of Unverified accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

df_unverified_peak_submissions

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
4018	t3_8vtmib	/r/NoFilterNews/comments/8vtmib/amber_heard_fa...	Amber Heard fans upset after she posted a raci...	NaN	r/NoFilterNews	2018-07-03 16:33:19	Neutral	Negative	1.0	NaN	...	True	False	False	58.0	33057.0	2013-08-24 19:00:23	unverified	others	1773 days 21:32:56	1773.0
4111	t3_8vwekz	/r/awfuleverything/comments/8vwekz/is_it_just_...	Is it just me? (Amber Heard)	NaN	r/awfuleverything	2018-07-03 22:20:41	Neutral	Neutral	19.0	NaN	...	False	False	False	4587.0	149.0	2016-01-14 00:00:25	unverified	others	901 days 22:20:16	901.0

2 rows × 24 columns

peak_day

'2018-07-03'

Unverified Accounts made 2 submissions on the peak day 2018-07-03

Which dates had the highest contrbitions of unverified accounts?

• Unverified Accounts With The Largest Link Karma
• Unverified Accounts With The Largest Comment Karma


• Unverified Accounts With The Minimum Link Karma
• Unverified Accounts With The Minimum Comment Karma


• Explore the largest scores
• Explore the minimum scores


• Contributions of Unverified accounts in 2018
• Creation years of the unverified accounts contributed in 2018
• Unverified Contributions Peaks

# group by date an count
unverified_contributions = df_unverified.groupby(df_unverified.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(unverified_contributions,
             x='created_at', y='n_contributions')

fig.update_layout(
            title={
        'text': "The number of contributions by users with unverified mails",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1, opacity=1, textposition='auto').update_layout()
fig.show()

# sort by n_contributions, take the top 3, then sort them by date 
unverified_contributions.sort_values('n_contributions', ascending=False, inplace=True)
unverified_trendy = unverified_contributions.head(5)
unverified_trendy.sort_values('created_at', inplace=True)

fig = px.bar(unverified_trendy,
             x='created_at', y='n_contributions')

fig.update_layout(
            title={
        'text': "The number of unverified users contributions on peak dates",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = unverified_trendy.created_at,
    )
)

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()

unverified_trendy

	created_at	n_contributions
68	2018-06-06	22
84	2018-07-03	21
109	2018-08-01	22
188	2018-12-19	38
189	2018-12-20	28

New Vs Others

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Contributions

fig = px.pie(df_merged.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = 'Contributions of 2018 accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

fig = px.pie(df_peak.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = f'Contributions of 2018 accounts on the peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

Submissions

fig = px.pie(df_submissions.creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = 'Submissions of  2018 accounts in 2018')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

NOTE: About 12% of 2018 Submissions were made by users created in 2018.

fig = px.pie(df_peak.query("submission_comment == 'submission'").creation_year.value_counts().to_frame().reset_index(),
             values='creation_year', names='index', color_discrete_sequence = colors,
             title = f'Submissions of 2018 accounts on the peak day ({peak_day})')

fig.update_traces(textposition='inside', textinfo='percent+label+value')

fig.show()

df_peak.shape[0]

190

df_peak.query("submission_comment == 'submission'").shape[0]

30

190-30

160

*NOTE:** We only have 30 submissions on peak day with about 160 comments and replies

New Accounts

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Accounts created in 2018

• Accounts created in 2018

Contributions

print('Total contributions in 2018 made by newly created accounts(2018): ',df_18.shape[0])
contr_prop_18 = df_18.shape[0] * 100 /df_merged.shape[0]

print('The percentage % of 2018 contributions made by newly created accounts(2018): ',round(contr_prop_18,2))

Total contributions in 2018 made by newly created accounts(2018):  875
The percentage % of 2018 contributions made by newly created accounts(2018):  12.51

Comments

print('Total comments in 2018 made by newly created accounts(2018): ', df_18.query(" submission_comment == 'comment' ").shape[0])

Total comments in 2018 made by newly created accounts(2018):  636

Submissions

print('Total submissions in 2018 made by newly created accounts(2018): ', df_18.query(" submission_comment == 'submission' ").shape[0])
sub_prop_18 = df_18.query(" submission_comment == 'submission' ").shape[0] * 100\
/df_merged.query(" submission_comment == 'submission' ").shape[0]


print('The percentage % of 2018 submissions made by newly created accounts(2018): ', sub_prop_18)

Total submissions in 2018 made by newly created accounts(2018):  239
The percentage % of 2018 submissions made by newly created accounts(2018):  11.95

2018 Contributions Scores

• Banned / Unverified
• Banned Accounts


• Unverified Accounts


• New Vs Others
• New Accounts


• 2018 Contributions Scores

Explore the largest scores

df_merged.score.describe()

count    6987.000000
mean       36.403750
std       170.110128
min       -92.000000
25%         1.000000
50%         3.000000
75%        16.000000
max      4188.000000
Name: score, dtype: float64

# Filter on largest scores

df_scores_high = df_merged.sort_values('score', ascending=False).head(10)

fig = px.bar(df_scores_high,
             x='user_name', 
             y=df_scores_high.score, text = df_scores_high.score, title='Accounts with highest contribution scores in 2018')

fig.update_layout(
    xaxis = dict(
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_high.user_name,
    )
)

clrs = ['red' if (y > 3500) else '#5296dd' for y in df_scores_high.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_scores_high.sort_values('score', ascending=False).user_name

579                -banned-
1134               -banned-
2426               emilyguy
369                -banned-
6936    NaughtyJessThoughts
2429               emilyguy
410                -banned-
182                -banned-
5748           fakeposter77
4424               Ezio9619
Name: user_name, dtype: object

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_merged[df_merged.user_name == 'emilyguy'])

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2426	t3_82wa5c	/r/gentlemanboners/comments/82wa5c/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-03-08 09:30:44	Neutral	Neutral	3814.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	786 days 14:47:55	786.0
2427	t3_842r8g	/r/gentlemanboners/comments/842r8g/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-03-13 09:09:02	Neutral	Neutral	442.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	791 days 14:26:13	791.0
2428	t3_85j0o6	/r/gentlemanboners/comments/85j0o6/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-03-19 12:18:13	Neutral	Neutral	595.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	797 days 17:35:24	797.0
2429	t3_8694b7	/r/WatchItForThePlot/comments/8694b7/amber_heard_has_an_amazing_body/	Amber Heard has an amazing body	NaN	r/WatchItForThePlot	2018-03-22 05:16:10	Positive	Positive	3165.0	NaN	submission	amber_heard_has_an_amazing_body	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	800 days 10:33:21	800.0
2430	t3_8694cl	/r/celebsnaked/comments/8694cl/amber_heard/	Amber Heard	NaN	r/celebsnaked	2018-03-22 05:16:21	Neutral	Neutral	316.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	800 days 10:33:32	800.0
2431	t3_8694ge	/r/celebnsfw/comments/8694ge/amber_heard/	Amber Heard	NaN	r/celebnsfw	2018-03-22 05:17:00	Neutral	Neutral	440.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	800 days 10:34:11	800.0
2432	t1_dw3ksem	/r/WatchItForThePlot/comments/8694b7/amber_heard_has_an_amazing_body/dw3ksem/	The Informers (2008)	t1_dw3k8b6	r/WatchItForThePlot	2018-03-22 11:29:16	Neutral	Neutral	12.0	comment	comment	amber_heard_has_an_amazing_body	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	800 days 16:46:27	800.0
2433	t1_dw4zgth	/r/celebnsfw/comments/8694ge/amber_heard/dw4zgth/	The Informers (2008)	t1_dw4xwws	r/celebnsfw	2018-03-23 01:35:27	Neutral	Neutral	1.0	comment	comment	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	801 days 06:52:38	801.0
2434	t3_86sot8	/r/gentlemanboners/comments/86sot8/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-03-24 12:22:42	Neutral	Neutral	158.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	802 days 17:39:53	802.0
2435	t3_8c2dua	/r/Celebs/comments/8c2dua/amber_heard/	Amber Heard	NaN	r/Celebs	2018-04-13 20:33:21	Neutral	Neutral	418.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	823 days 01:50:32	823.0
2436	t1_dxbiw14	/r/Celebs/comments/8c2dua/amber_heard/dxbiw14/	The Playboy Club (2011)	t3_8c2dua	r/Celebs	2018-04-13 20:33:45	Neutral	Neutral	4.0	submission	comment	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	823 days 01:50:56	823.0
2437	t3_8gbrwm	/r/gentlemanboners/comments/8gbrwm/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-05-01 20:47:43	Neutral	Neutral	77.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	841 days 02:04:54	841.0
2438	t3_8hb267	/r/celebsnaked/comments/8hb267/amber_heard/	Amber Heard	NaN	r/celebsnaked	2018-05-05 22:51:05	Neutral	Neutral	147.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	845 days 04:08:16	845.0
2439	t3_8hb2g4	/r/celebnsfw/comments/8hb2g4/amber_heard/	Amber Heard	NaN	r/celebnsfw	2018-05-05 22:52:18	Neutral	Neutral	509.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	845 days 04:09:29	845.0
2440	t3_8hb2jx	/r/Celebs/comments/8hb2jx/amber_heard/	Amber Heard	NaN	r/Celebs	2018-05-05 22:52:45	Neutral	Neutral	516.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	845 days 04:09:56	845.0
2441	t1_dyjcjj9	/r/Celebs/comments/8hb2jx/amber_heard/dyjcjj9/	The Informers	t1_dyjceib	r/Celebs	2018-05-06 14:34:19	Neutral	Neutral	3.0	comment	comment	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	845 days 19:51:30	845.0
2442	t3_8jvhpz	/r/gentlemanboners/comments/8jvhpz/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-05-16 14:48:15	Neutral	Neutral	231.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	855 days 20:05:26	855.0
2443	t3_8m0z5o	/r/gentlemanboners/comments/8m0z5o/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-05-25 11:21:36	Neutral	Neutral	617.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	864 days 16:38:47	864.0
2444	t3_8qcee6	/r/gentlemanboners/comments/8qcee6/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-06-11 19:47:28	Neutral	Neutral	195.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	882 days 01:04:39	882.0
2445	t3_8sc5fj	/r/gentlemanboners/comments/8sc5fj/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-06-19 20:24:11	Neutral	Neutral	103.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	890 days 01:41:22	890.0
2446	t3_8ujtgq	/r/gentlemanboners/comments/8ujtgq/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-06-28 14:16:46	Neutral	Neutral	11.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	898 days 19:33:57	898.0
2447	t3_8vqlv6	/r/Celebs/comments/8vqlv6/amber_heard/	Amber Heard	NaN	r/Celebs	2018-07-03 08:38:02	Neutral	Neutral	1041.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	903 days 13:55:13	903.0
2448	t3_8vzeyv	/r/gentlemanboners/comments/8vzeyv/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-07-04 06:20:16	Neutral	Neutral	93.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	904 days 11:37:27	904.0
2449	t3_8wec1y	/r/gentlemanboners/comments/8wec1y/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-07-05 21:32:00	Neutral	Neutral	494.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	906 days 02:49:11	906.0
2450	t3_8y7edh	/r/gentlemanboners/comments/8y7edh/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-07-12 06:42:02	Neutral	Neutral	119.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	912 days 11:59:13	912.0
2451	t3_91b2de	/r/gentlemanboners/comments/91b2de/amber_heard_and_jessica_alba/	Amber Heard and Jessica Alba	NaN	r/gentlemanboners	2018-07-23 21:15:41	Neutral	Neutral	316.0	NaN	submission	amber_heard_and_jessica_alba	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	924 days 02:32:52	924.0
2452	t3_98f1dv	/r/gentlemanboners/comments/98f1dv/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-08-18 21:18:18	Neutral	Neutral	276.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	950 days 02:35:29	950.0
2453	t3_9aikvm	/r/gentlemanboners/comments/9aikvm/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-08-26 20:11:26	Neutral	Neutral	241.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	958 days 01:28:37	958.0
2454	t3_9goioy	/r/gentlemanboners/comments/9goioy/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-09-17 21:30:38	Neutral	Neutral	58.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	980 days 02:47:49	980.0
2455	t3_9iwj13	/r/gentlemanboners/comments/9iwj13/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-09-25 21:58:08	Neutral	Neutral	206.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	988 days 03:15:19	988.0
2456	t3_9ligl2	/r/Celebs/comments/9ligl2/amber_heard/	Amber Heard	NaN	r/Celebs	2018-10-05 02:39:33	Neutral	Neutral	206.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	997 days 07:56:44	997.0
2457	t3_9lihq6	/r/celebsnaked/comments/9lihq6/amber_heard/	Amber Heard	NaN	r/celebsnaked	2018-10-05 02:43:57	Neutral	Neutral	266.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	997 days 08:01:08	997.0
2458	t3_9oym7g	/r/gentlemanboners/comments/9oym7g/amber_heard/	Amber Heard	NaN	r/gentlemanboners	2018-10-17 13:22:22	Neutral	Neutral	225.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1009 days 18:39:33	1009.0
2459	t3_9t9agd	/r/Celebs/comments/9t9agd/amber_heard/	Amber Heard	NaN	r/Celebs	2018-11-01 13:12:56	Neutral	Neutral	193.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1024 days 18:30:07	1024.0
2460	t3_9xew1l	/r/Celebs/comments/9xew1l/amber_heard/	Amber Heard	NaN	r/Celebs	2018-11-15 20:22:16	Neutral	Neutral	448.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1039 days 01:39:27	1039.0
2461	t3_a6qp82	/r/Celebs/comments/a6qp82/amber_heard/	Amber Heard	NaN	r/Celebs	2018-12-16 17:07:53	Neutral	Neutral	164.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1069 days 22:25:04	1069.0
2462	t3_a8n49w	/r/CaraDelevingne/comments/a8n49w/with_amber_heard/	with Amber Heard	NaN	r/CaraDelevingne	2018-12-22 18:26:05	Neutral	Neutral	272.0	NaN	submission	with_amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1075 days 23:43:16	1075.0
2463	t3_aaot4i	/r/celebsnaked/comments/aaot4i/amber_heard/	Amber Heard	NaN	r/celebsnaked	2018-12-29 20:44:00	Neutral	Neutral	389.0	NaN	submission	amber_heard	emilyguy	True	True	False	False	26585.0	1836704.0	2016-01-11 18:42:49	others	others	1083 days 02:01:11	1083.0

df_merged[df_merged.user_name == 'emilyguy'].submission_comment.value_counts()

submission    34
comment        4
Name: submission_comment, dtype: int64

df_merged[df_merged.user_name == 'emilyguy'].subreddit.value_counts()

r/gentlemanboners      19
r/Celebs                9
r/celebsnaked           4
r/celebnsfw             3
r/WatchItForThePlot     2
r/CaraDelevingne        1
Name: subreddit, dtype: int64

df_merged[df_merged.user_name == 'emilyguy'].created_at.dt.date.value_counts()

2018-03-22    4
2018-05-05    3
2018-10-05    2
2018-04-13    2
2018-07-03    1
2018-08-18    1
2018-09-17    1
2018-03-19    1
2018-05-06    1
2018-05-01    1
2018-05-25    1
2018-07-23    1
2018-03-08    1
2018-07-04    1
2018-06-19    1
2018-03-23    1
2018-05-16    1
2018-12-22    1
2018-06-28    1
2018-06-11    1
2018-07-05    1
2018-08-26    1
2018-07-12    1
2018-10-17    1
2018-09-25    1
2018-11-15    1
2018-11-01    1
2018-12-16    1
2018-03-24    1
2018-12-29    1
2018-03-13    1
Name: created_at, dtype: int64

df_merged[df_merged.user_name == 'emilyguy'].shape

(38, 24)

emilyguy

• Created on 2018-03-08
• Has only 38 contributions in 2018
  • submission 34
  • comment 4
• in those subreddits
  • r/gentlemanboners 19
  • r/Celebs 9
  • r/celebsnaked 4
  • r/celebnsfw 3
  • r/WatchItForThePlot 2
  • r/CaraDelevingne 1

Explore the minimum scores

# Filter on minimum scores

df_scores_low = df_merged.sort_values('score').head(20)

fig = px.bar(df_scores_low,
             x='user_name', 
             y=df_scores_low.score, 
             text = df_scores_low.score)

fig.update_layout(title_text='Accounts with minimum contribution scores in 2018', title_x=0.5, title_y=0.2)

fig.update_layout(
    xaxis = dict(
        side='top',
        title='user name',
        tickmode = 'array',
        tickvals = df_scores_low.user_name,
    )
)

clrs = ['red' if (y < -60) else '#5296dd' for y in df_scores_low.score]

fig.update_traces(marker_color=clrs,
                  marker_line_width=2, opacity=1, textposition='auto')


fig.show()

df_scores_low.user_name.head(5)

2467    MedivhTheEternal
807             -banned-
2468    MedivhTheEternal
5754           sanjuhunk
3856        HardSellDude
Name: user_name, dtype: object

with pd.option_context('display.max_colwidth', None, 'display.max_columns', None):
  display(df_merged[df_merged.user_name == 'MedivhTheEternal'])

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	submission_text	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2465	t1_dvddsca	/r/gentlemanboners/comments/82wa5c/amber_heard/dvddsca/	She may be beautiful but shes just a high quality gold digger... screw her.. First Johnny Depp and then Elon Musk...	t3_82wa5c	r/gentlemanboners	2018-03-08 12:06:55	Positive	Neutral	115.0	submission	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	276 days 04:53:51	276.0
2466	t1_dvdj8yw	/r/gentlemanboners/comments/82wa5c/amber_heard/dvdj8yw/	Fair enough i guess :)	t1_dvdj7qq	r/gentlemanboners	2018-03-08 14:20:19	Positive	Positive	23.0	comment	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	276 days 07:07:15	276.0
2467	t1_dvdlu66	/r/gentlemanboners/comments/82wa5c/amber_heard/dvdlu66/	I’ll have to disagree man, im a huge fan of Johnny Depp no matter what i think hes right. I am biased perhaps but i cant help it	t1_dvdllh0	r/gentlemanboners	2018-03-08 15:07:07	Positive	Neutral	-92.0	comment	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	276 days 07:54:03	276.0
2468	t1_dvdm05m	/r/gentlemanboners/comments/82wa5c/amber_heard/dvdm05m/	I think marrying for money is a more dishonored action	t1_dvdlwtp	r/gentlemanboners	2018-03-08 15:09:57	Positive	Neutral	-66.0	comment	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	276 days 07:56:53	276.0
2469	t1_dvdmfpp	/r/gentlemanboners/comments/82wa5c/amber_heard/dvdmfpp/	I dont play Hearthstone, I play WoW	t1_dvdme6s	r/gentlemanboners	2018-03-08 15:17:11	Positive	Neutral	-5.0	comment	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	276 days 08:04:07	276.0
2470	t1_dvxmtip	/r/gentlemanboners/comments/85aijy/amber_heard/dvxmtip/	I completely agree	t1_dvwjw4v	r/gentlemanboners	2018-03-19 10:34:42	Positive	Positive	1.0	comment	comment	amber_heard	MedivhTheEternal	True	False	False	False	687.0	6957.0	2017-06-05 07:13:04	others	others	287 days 03:21:38	287.0

MedivhTheEternal

• Created on 2018-03-08
• Has the minimum contribution scores in 2018

for i in df_merged[df_merged.user_name == 'MedivhTheEternal'].text.values:
    print('-', i)

- She may be beautiful but shes just a high quality gold digger... screw her.. First Johnny Depp and then Elon Musk...
- Fair enough i guess :)
- I’ll have to disagree man, im a huge fan of Johnny Depp no matter what i think hes right. I am biased perhaps but i cant help it
- I think marrying for money is a more dishonored action
- I dont play Hearthstone, I play WoW
- I completely agree

Conclusions

• Introduction
• Exploratory Data Analysis
• Conclusions

Banned / Unverified Accounts

• About 20% of 2018 contributions were made by banned (8.38% --> 586) and unverified (11.3% --> 789) accounts.
• About 20% of 2020 submissions were made by banned (14.9% --> 1175) and unverified (6.48% -> 511) accounts.
• 28.5% of the peak day contributions (2018-07-03) were made by banned (17.4% --> 33 and unverified (11.1% --> 21) accounts.
• 21.45% of submissions of the peak day submissions (2018-07-03) were made by banned accounts(15.3% --> 305) and unverified accounts (6.15% --> 123).

Banned

fakeposter77 only one submission in 11-11-2018.
fakeposter77: this user contributions got the hieghest scores.
michikoperdue: this user made 3 contributions with the same text in the same time in 3 different subreddits

• r/Celebsnudess 1
• r/CelebNudes 1
• r/celebJObuds 1

Unverified

Unverified Accounts Contributions in 2018

Accounts Creation Year	n_contributions
2018	33.6% (265)
others	66.4% (524)

Unverified Accounts Submissions in 2018

Accounts Creation Year	n_submissions
2018	57.7% (71)
others	42.3% (52)

Unverified Accounts Contributions in 2018 peak day (03/07/2018)

Accounts Creation Year	n_contributions
2018	33.3% (7)
others	66.7% (14)

Unverified Accounts Submissions in 2018 peak day (03/07/2018)

Accounts Creation Year	n_submissions
2018	57.7% (71)
others	42.3% (52)

hnglmkrnglbrry unverified account with the highest score.

• Account Created at: 2017-01-30

A lot of unverified users has a link karma of 1

• 'nsfwalt_100', 'EverGenius', 'windinmynuts', 'giantfatdelicousbird', 'epicimagebot', 'IWannaBangBiel', 'IamsWhoIams', 'Tedmonds30', 'juggalobobby1', 'Sonmeisterbank', 'MrGreen098', 'Ragobie0404'

murph420 has the minimum comment karma
ibracadabra101 this user has the highest score among the unverified users
HardSellDude this user has the minimum score among the unverified users

2018 Accounts

• 12.5% of 2018 contributions were made by users created in 2018.

• 11.9% of 2018 submissions were made by users created in 2018.

• 7.89% of the peak day contributions (03/07/2018) were made by users created in 2018.
• 3.33% of the peak day submissions (03/07/2018) were made by users created in 2018.

2018 Accounts Contributions in 2018

Accounts Creation Year	n_contributions
2018	12.5% (875)
banned	8.38% (586)
others	79.1% (5532)

2018 Accounts Submissions in 2018

Accounts Creation Year	n_submissions
2018	11.9% (239)
banned	15.3% (305)
others	72.8% (1456)

2018 Accounts Contributions in 2018 peak day (07/03/2018)

Accounts Creation Year	n_contributions
2018	7.89% (15)
banned	17.4% (33)
others	74.7% (142)

2018 Accounts Submissions in 2018 peak day (07/03/2018)

Accounts Creation Year	n_submissions
2018	3.33% (1)
banned	33.3% (10)
others	63.3% (19)

emilyguy

• Created on 2018-03-08
• Has only 38 contributions in 2018
  • submission 34
  • comment 4
• in those subreddits
  • r/gentlemanboners 19
  • r/Celebs 9
  • r/celebsnaked 4
  • r/celebnsfw 3
  • r/WatchItForThePlot 2
  • r/CaraDelevingne 1

MedivhTheEternal

• Created on 2018-03-08
• Has the minimum contribution scores in 2018
  • She may be beautiful but shes just a high quality gold digger... screw her.. First Johnny Depp and then Elon Musk...
  • Fair enough i guess :)
  • I’ll have to disagree man, im a huge fan of Johnny Depp no matter what i think hes right. I am biased perhaps but i cant help it
  • I think marrying for money is a more dishonored action
  • I dont play Hearthstone, I play WoW
  • I completely agree